Prabir Das

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Don Ross

University College Cork

Bob Jessop

Lancaster University

Alejandra B Osorio

Wellesley College

Armando Marques-Guedes

UNL - New University of Lisbon

Giulia Sissa

Ucla

Florin Curta

University of Florida

Mauro Grondona

University of Genova

Daniel Hershenzon

University of Connecticut

Derek H Alderman

University of Tennessee Knoxville

Ian G Baird

University of Wisconsin-Madison

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Retrieval of biophysical parameters for cotton and rice crops using Resourcesat-2 AWiFS data in Nalgonda district, Andhra Pradesh, India

Journal of Spatial Science, Apr 24, 2015

In the present study, a regression-based approach was adopted for retrieval of leaf-area index (L... more In the present study, a regression-based approach was adopted for retrieval of leaf-area index (LAI) and chlorophyll-content index (CCI) using Resourcesat-2 AWiFS data. Multi-temporal satellite data were utilised to generate cotton and rice crop masks. Three vegetation indices (VIs) were computed from AWiFS data, which were further regressed with ground-measured LAI and CCI values to generate empirical models along with their regression parameters. The best-fit models were adopted for inversion to generate crop-specific spatial maps of biophysical parameters for both cotton and rice crops. The geo-spatial products of crop biophysical parameters were further validated using in-situ observations.

Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data

Geocarto International, 2014

The use of multispectral satellite sensors for generation of hyperspectral indices is restricted ... more The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI705), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI705, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement.

Alternating eulerian trails with prescribed degrees in two edge-colored complete graphs

Discrete Mathematics, 1983

Let K,, be the complete graph with vertex set {u,, u2,. . . , u,,) and let g = (gl, . . . , g,,) ... more Let K,, be the complete graph with vertex set {u,, u2,. . . , u,,) and let g = (gl, . . . , g,,) be a sequence of positive integers. Color each edge of this K, red or blue. In this paper necessary and sufficient conditions are given which guarantee the existence of a connected span&g subgraph F in K,, (as colored) with both red degree and blue degree in F at vertex Ui eqa$ to gi. When each & = 1 this answers a question of Erdiis proved in this special case in [I].

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Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach

International Journal of Applied Earth Observation and Geoinformation, 2015

In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinen... more In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L.) polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wavelengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and multi-variate analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0. A significant improvement in R 2 values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R 2 = 0.81 and RMSE = 1.39 mg g -1 ) among all the uni-and multivariate analysis for predicting the polyphenol of fresh tea leaves.

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Retrieval of biophysical parameters for cotton and rice crops using Resourcesat-2 AWiFS data in Nalgonda district, Andhra Pradesh, India

Journal of Spatial Science, Apr 24, 2015

Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data

Geocarto International, 2014

Alternating eulerian trails with prescribed degrees in two edge-colored complete graphs

Discrete Mathematics, 1983

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Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach

International Journal of Applied Earth Observation and Geoinformation, 2015

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